Opposition-Based Differential Evolution Algorithms

  title={Opposition-Based Differential Evolution Algorithms},
  author={Shahryar Rahnamayan and Hamid R. Tizhoosh and Magdy M. A. Salama},
  journal={2006 IEEE International Conference on Evolutionary Computation},
Evolutionary Algorithms (EAs) are well-known optimization approaches to cope with non-linear, complex problems. These population-based algorithms, however, suffer from a general weakness; they are computationally expensive due to slow nature of the evolutionary process. This paper presents some novel schemes to accelerate convergence of evolutionary algorithms. The proposed schemes employ opposition-based learning for population initialization and also for generation jumping. In order to… CONTINUE READING
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